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The Internet of Vehicles (IoV) is employed to gather real-time traffic information for drivers, and base stations in 5G systems are used to assist in traffic data transmission. For rapid implementation, the applications in vehicles are available to be offloaded to edge nodes (ENs) which are enhanced from micro base stations. Despite the benefits of IoV and ENs, the explosive growth of offloaded vehicle applications exceeds the capacity of ENs, causing the overload of fractional ENs. Therefore, it is necessary to offload the computing applications in overloaded ENs to other idle ENs, while it is a challenge to select appropriate offloading destination ENs. In this paper, we first consider edge computing framework for computation offloading in IoV under the architecture of 5G networks. We then formulate a multi-objective optimization problem to select suitable destination ENs, which aims to minimize the vehicle application offloading delay and offloading cost as well as realizing the load balance of ENs. Moreover, a computation offloading method for IoV, named COV, is designed to solve the multi-objective optimization problem. Finally, various simulation analyses demonstrate the effectiveness and efficiency of COV. [ABSTRACT FROM AUTHOR]

• By analysing the motion magnitude of the videos, the proposed spatiotemporal interest points algorithm could eliminate a lot of superuous vehicle video at the edge. • The video segmentation algorithm based on the multi-modal linear features combination divides the video sequence into segments of interests, and then extracts the video clips from these segments. • An optimized YOLOv3 vehicle detection algorithm is presented based on edge computing, which greatly improves the detection speed and further meets the low latency and high accuracy requirements. • The extensive numerical experimental verification results show our methods may achieve better performance, compared with the existing algorithms for different stages of the redundancy elimination, video segmentation, key frame selection and vehicle detection. In the Internet of Things enabled intelligent transportation systems, a huge amount of vehicle video data has been generated and real-time and accurate video analysis are very important and challenging work, especially in situations with complex street scenes. Therefore, we propose edge computing based video pre-processing to eliminate the redundant frames, so that we migrate the partial or all the video processing task to the edge, thereby diminishing the computing, storage and network bandwidth requirements of the cloud center, and enhancing the effectiveness of video analyzes. To eliminate the redundancy of the traffic video, the magnitude of motion detection based on spatio-temporal interest points (STIP) and the multi-modal linear features combination are presented which splits a video into super frame segments of interests. After that, we select the key frames from these interesting segments of the long videos with the design and detection of the prominent region. Finally, the extensive numerical experimental verification results show our methods are superior to the previous algorithms for different stages of the redundancy elimination, video segmentation, key frame selection and vehicle detection. [ABSTRACT FROM AUTHOR]

Edge computing enables portable devices to provide smart applications, and the indoor positioning technique offers accurate location-based indoor navigation and personalized smart services. To achieve the high positioning accuracy, an indoor positioning algorithm based on particle filter requires a large number of sample particles to approximate the probability density function, which leads to the additional computational cost and high fusion delay. Focusing on real-time and accurate positioning, an edge computing-enabled green multi-source fusion indoor positioning algorithm called APFP is proposed based on adaptive particle filter in this paper. APFP considers both pedestrian dead reckoning (PDR) signals in mobile terminals and the received signal strength indication (RSSI) of Bluetooth, and effectively merges the error-free accumulation of trilateral positioning and the accurate short-range positioning of PDR, which enables mobile terminals adaptively perform particle filter to reduce the computing time and power consumption while ensuring positioning accuracy simultaneously. Detailed experimental results show that, compared with the traditional particle filter algorithm and the map-constrained algorithm, the proposed APFP reduces fusion computing cost by 59.89% and 54.37%, respectively. [ABSTRACT FROM AUTHOR]

Currently, 6G is being proposed as a new next-generation communication architecture. The inclusion of satellite communication networks makes it possible to genuinely space-air-ground integrated networks, especially the combination of 6G with machine learning algorithms, which has attracted increasing attention. Of particular note is the combination of collaborative learning with the Internet of Everything, which improves the intelligence and collaboration of devices with more robust privacy protection, higher computing power, and greater energy savings. However, the airspace environment in 6G is extraordinarily dynamic and delay-sensitive for services, and traditional federation learning is a synchronous aggregation model with slow convergence. To address this problem, the paper proposes a hierarchical satellite-ground collaborative architecture with three roles: remote cloud center, orbital edge computing server, and data node. We use the advantages of orbital edge computing and low-orbit satellite network communication to manage the parameter transfer problem during the collaboration process. Meanwhile, we propose an asynchronous adaptive collaborative aggregation algorithm (AFLS) for the satellite-ground architecture, enabling the federated members to update the model parameters in time during the dynamic joining aggregation process. Experiments show that our architecture and algorithm excel in training performance with 95 % accuracy and fewer communication rounds in image classification when the roles are in good collaboration. [ABSTRACT FROM AUTHOR]

In recent years, the use of edge computing to solve the problem of limited resources in the IoV has attracted more and more attention. Vehicles can upload tasks to the edge servers within their radio range for computing cooperation and offloading. However, due to shared resources among edge servers, the uneven distribution of vehicles may lead to many problems such as uneven tasks distribution, unbalanced load, and low computing efficiency on different edge servers. On the other hand, most of the existing works are proposed to address the load unbalance issue of edge servers with the help of remote cloud or vehicle cloud, but still leading to resource wasting problems especially for edge servers with a light load. To address this issue, a task transfer scheme has been proposed among different edge servers in this paper. First, we designed a partitioned and hierarchical software-defined network architecture for IoV. After that, a load balancing model is proposed under this network architecture, in which the global controller can manage and schedule the local tasks from each edge server. Next, we proved that the task allocation problem for our load balancing model under the proposed network architecture is an NP-hard problem. To solve this problem, a Deep Q-Network (DQN) model is proposed to minimize the mean square deviation of loads among different edge servers. Numerical results show that our proposed load balancing model can significantly improve the resource utilization of edge servers as well as reduce the computational latency of vehicular tasks. [ABSTRACT FROM AUTHOR]

Nowadays, due to the advances in mobile and wireless communication, mobile devices are widely used in our daily life. Meanwhile, in the mobile devices, there exits diverse applications which are developed to satisfy the various requirements of mobile users. Correspondingly, a large number of services are produced by the mobile devices. Since the mobile devices have limitations on the battery capacity, physical size, etc., they can hardly complete all the services. To relieve this problem, driven by edge computing, the central units (CUs) in fifth-generation wireless systems (5G) could be enhanced into edge nodes (ENs) for processing. However, during the transmission of edge services, the privacy leakage may occur, and the overall performance of the networks needs to be taken into consideration. In this paper, an optimization problem is defined to improve the resource utilization and load balance for all the ENs while protecting the privacy information and satisfying the time requirement. Then, a balanced service offloading method, abbreviated BSOM, is proposed. Finally, abundant experiments and evaluations are conducted to validate our proposed method is both effective and feasible. [ABSTRACT FROM AUTHOR]

Thanks to the explosive evolution of 6G, Artificial intelligence, Blockchain, Cloud computing, Data, and Edge computing (6G+ABCDE), it is possible to make Intelligent Transportation Systems (ITS) more autonomous, intelligent, and efficient. Conventional ITS technologies have been applied and deployed worldwide, and autonomous vehicles have also made a qualitative leap. The revolution of ITS magnitude is on the horizon, and the singularity may happen in the 6G Autonomous Intelligent Transportation Systems (6G-AITS). We indicate that the human-centric transportation system will still be the key to 6G-AITS. The 6G-AITS is a three-dimensional intelligent system, which is driven by data and intelligence, and characterized by sharing and collaboration, thereby achieving the service-oriented demand response. To support this vision, we explore the 6G-AITS changes in terms of system architecture, communication, and resource allocation, as well as its key features. We elaborate on the mechanism and three applications of 6G-AITS. Furthermore, we explore the challenges of 6G-AITS and consider some issues that go beyond technologies that could affect the development of 6G-AITS. Key objective of this work is to provide an exploration of the imperative requirements to develop 6G-AITS and guidance for the research and development of 6G-AITS. [ABSTRACT FROM AUTHOR]